Abstract

Infection by pathogenic bacteria on implanted and indwelling medical devices during surgery causes large morbidity and mortality worldwide. Attempts to ameliorate this important medical issue have included development of antimicrobial surfaces on materials, ‘no touch’ surgical procedures, and development of materials with inherent low pathogen attachment. The search for new materials is increasingly being carried out by high throughput methods. Efficient methods for extracting knowledge from these large data sets are essential. We used data from a large polymer microarray exposed to three clinical pathogens to derive robust and predictive machine-learning models of pathogen attachment. The models could predict pathogen attachment for the polymer library quantitatively. The models also successfully predicted pathogen attachment for a second-generation library, and identified polymer surface chemistries that enhance or diminish pathogen attachment.

Item Type:

Article

Additional Information:

This is the pre-peer reviewed version of the following article: Epa, V. C., Hook, A. L., Chang, C., Yang, J., Langer, R., Anderson, D. G., Williams, P., Davies, M. C., Alexander, M. R. and Winkler, D. A. (2014), Modelling and Prediction of Bacterial Attachment to Polymers. Advanced Functional Materials, 24: 2085-2093. doi: 10.1002/adfm.201302877 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/adfm.201302877/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.